Behavior-Aware Online Prediction of Obstacle Occupancy using Zonotopes
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909865190883328 |
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| author | Carrizosa-Rendon, Alvaro Zhou, Jian Frisk, Erik Puig, Vicenc Nejjari, Fatiha |
| author_facet | Carrizosa-Rendon, Alvaro Zhou, Jian Frisk, Erik Puig, Vicenc Nejjari, Fatiha |
| contents | Predicting the motion of surrounding vehicles is key to safe autonomous driving, especially in unstructured environments without prior information. This paper proposes a novel online method to accurately predict the occupancy sets of surrounding vehicles based solely on motion observations. The approach is divided into two stages: first, an Extended Kalman Filter and a Linear Programming (LP) problem are used to estimate a compact zonotopic set of control actions; then, a reachability analysis propagates this set to predict future occupancy. The effectiveness of the method has been validated through simulations in an urban environment, showing accurate and compact predictions without relying on prior assumptions or prior training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Behavior-Aware Online Prediction of Obstacle Occupancy using Zonotopes Carrizosa-Rendon, Alvaro Zhou, Jian Frisk, Erik Puig, Vicenc Nejjari, Fatiha Systems and Control Robotics Predicting the motion of surrounding vehicles is key to safe autonomous driving, especially in unstructured environments without prior information. This paper proposes a novel online method to accurately predict the occupancy sets of surrounding vehicles based solely on motion observations. The approach is divided into two stages: first, an Extended Kalman Filter and a Linear Programming (LP) problem are used to estimate a compact zonotopic set of control actions; then, a reachability analysis propagates this set to predict future occupancy. The effectiveness of the method has been validated through simulations in an urban environment, showing accurate and compact predictions without relying on prior assumptions or prior training data. |
| title | Behavior-Aware Online Prediction of Obstacle Occupancy using Zonotopes |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2510.20437 |